Robust Covariance Estimation for Data Fusion From Multiple Sensors

Show simple item record Sequeira, J. - Tsourdos, Antonios - Lazarus, S. - 2012-08-29T23:00:49Z 2012-08-29T23:00:49Z 2012-08-30
dc.identifier.issn 0018-9456 -
dc.identifier.uri -
dc.description.abstract This paper addresses the robust estimation of a covariance matrix to express uncertainty when fusing information from multiple sensors. This is a problem of interest in multiple domains and applications, namely, in robotics. This paper discusses the use of estimators using explicit measurements from the sensors involved versus estimators using only covariance estimates from the sensor models and navigation systems. Covariance intersection and a class of orthogonal Gnanadesikan-Kettenring estimators are compared using the 2-norm of the estimates. A Monte Carlo simulation of a typical mapping experiment leads to conclude that covariance estimation systems with a hybrid of the two estimators may yield the best results. en_UK
dc.language.iso en_UK -
dc.publisher IEEE Institute of Electrical and Electronics en_UK
dc.title Robust Covariance Estimation for Data Fusion From Multiple Sensors en_UK
dc.type Article -

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